






'''simple docstring'''





import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)


class  snake_case__      (					UpperCamelCase):




					def __init__(  self  :		str				, _A  :		List[Any]=0.01				, _A  :		List[Any]=10_00      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Any          =       p_stop
								UpperCAmelCase_      :       Any          =       max_length





					def __iter__(  self  :		List[str]      )    ->							Any:
								UpperCAmelCase_      :       Dict          =       0
								UpperCAmelCase_      :       List[Any]          =       False
								while not stop and count < self.max_length:
											yield count
											count += 1
											UpperCAmelCase_      :       Any          =       random.random() < self.p_stop






class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Any				, _A  :		Any				, _A  :		Optional[Any]				, _A  :		int=False				, _A  :		Dict=True      )    ->							int:
								UpperCAmelCase_      :       Tuple          =       [
								    BatchSamplerShard(_A				, 2				, _A				, split_batches=_A				, even_batches=_A      )
								    for i in range(2      )
								]
								UpperCAmelCase_      :       Tuple          =       [list(_A      ) for batch_sampler_shard in batch_sampler_shards]
								if not split_batches:
											self.assertListEqual([len(_A      ) for shard in batch_sampler_shards]				, [len(_A      ) for e in expected]      )
								self.assertListEqual(_A				, _A      )




					def 		A      (  self  :		Any      )    ->							Dict:
								# Check the shards when the dataset is a round multiple of total batch size.
								UpperCAmelCase_      :       List[str]          =       BatchSampler(range(24      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Optional[Any]          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
								]
								self.check_batch_sampler_shards(_A				, _A      )

								UpperCAmelCase_      :       str          =       BatchSampler(range(24      )				, batch_size=3				, drop_last=_A      )
								# Expected shouldn't change
								self.check_batch_sampler_shards(_A				, _A      )

								# Check the shards when the dataset is a round multiple of batch size but not total batch size.
								UpperCAmelCase_      :       str          =       BatchSampler(range(21      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Tuple          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
								]
								self.check_batch_sampler_shards(_A				, _A      )

								UpperCAmelCase_      :       str          =       BatchSampler(range(21      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Dict          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(_A				, _A      )

								# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
								# num_processes batch.
								UpperCAmelCase_      :       Union[str, Any]          =       BatchSampler(range(22      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       int          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
								]
								self.check_batch_sampler_shards(_A				, _A      )

								UpperCAmelCase_      :       Optional[Any]          =       BatchSampler(range(22      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Any          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(_A				, _A      )

								# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
								# num_processes batch.
								UpperCAmelCase_      :       Optional[Any]          =       BatchSampler(range(20      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       List[str]          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
								]
								self.check_batch_sampler_shards(_A				, _A      )

								UpperCAmelCase_      :       Any          =       BatchSampler(range(20      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       str          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(_A				, _A      )

								# Check the shards when the dataset is very small.
								UpperCAmelCase_      :       List[Any]          =       BatchSampler(range(2      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Any          =       [[[0, 1, 0]], [[1, 0, 1]]]
								self.check_batch_sampler_shards(_A				, _A      )

								UpperCAmelCase_      :       str          =       BatchSampler(range(2      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       [[], []]
								self.check_batch_sampler_shards(_A				, _A      )




					def 		A      (  self  :		Optional[Any]      )    ->							int:
								# Check the shards when the dataset is a round multiple of batch size.
								UpperCAmelCase_      :       int          =       BatchSampler(range(24      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Dict          =       [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
								]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A      )

								UpperCAmelCase_      :       Optional[Any]          =       BatchSampler(range(24      )				, batch_size=4				, drop_last=_A      )
								# Expected shouldn't change
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A      )

								# Check the shards when the dataset is not a round multiple of batch size.
								UpperCAmelCase_      :       Union[str, Any]          =       BatchSampler(range(22      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Optional[int]          =       [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
								]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A      )

								UpperCAmelCase_      :       List[str]          =       BatchSampler(range(22      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Any          =       [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A      )

								# Check the shards when the dataset is not a round multiple of batch size or num_processes.
								UpperCAmelCase_      :       str          =       BatchSampler(range(21      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       int          =       [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
								]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A      )

								UpperCAmelCase_      :       Dict          =       BatchSampler(range(21      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Any          =       [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A      )

								# Check the shards when the dataset is very small.
								UpperCAmelCase_      :       Tuple          =       BatchSampler(range(2      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Any          =       [[[0, 1]], [[0, 1]]]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A      )

								UpperCAmelCase_      :       Optional[Any]          =       BatchSampler(range(2      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       [[], []]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A      )




					def 		A      (  self  :		List[Any]      )    ->							Optional[int]:
								# Check the shards when the dataset is a round multiple of total batch size.
								UpperCAmelCase_      :       Tuple          =       BatchSampler(range(24      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Any          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
								]
								self.check_batch_sampler_shards(_A				, _A				, even_batches=_A      )

								UpperCAmelCase_      :       str          =       BatchSampler(range(24      )				, batch_size=3				, drop_last=_A      )
								# Expected shouldn't change
								self.check_batch_sampler_shards(_A				, _A				, even_batches=_A      )

								# Check the shards when the dataset is a round multiple of batch size but not total batch size.
								UpperCAmelCase_      :       List[Any]          =       BatchSampler(range(21      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Any          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(_A				, _A				, even_batches=_A      )

								UpperCAmelCase_      :       Optional[Any]          =       BatchSampler(range(21      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Tuple          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(_A				, _A				, even_batches=_A      )

								# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
								# num_processes batch.
								UpperCAmelCase_      :       Optional[Any]          =       BatchSampler(range(22      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Dict          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
								]
								self.check_batch_sampler_shards(_A				, _A				, even_batches=_A      )

								UpperCAmelCase_      :       Union[str, Any]          =       BatchSampler(range(22      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(_A				, _A				, even_batches=_A      )

								# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
								# num_processes batch.
								UpperCAmelCase_      :       Dict          =       BatchSampler(range(20      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Optional[int]          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(_A				, _A				, even_batches=_A      )

								UpperCAmelCase_      :       int          =       BatchSampler(range(20      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Tuple          =       [
								    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
								    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
								]
								self.check_batch_sampler_shards(_A				, _A				, even_batches=_A      )

								# Check the shards when the dataset is very small.
								UpperCAmelCase_      :       Union[str, Any]          =       BatchSampler(range(2      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       Optional[int]          =       [[[0, 1]], []]
								self.check_batch_sampler_shards(_A				, _A				, even_batches=_A      )

								UpperCAmelCase_      :       Tuple          =       BatchSampler(range(2      )				, batch_size=3				, drop_last=_A      )
								UpperCAmelCase_      :       int          =       [[], []]
								self.check_batch_sampler_shards(_A				, _A				, even_batches=_A      )




					def 		A      (  self  :		Dict      )    ->							Optional[Any]:
								# Check the shards when the dataset is a round multiple of batch size.
								UpperCAmelCase_      :       Dict          =       BatchSampler(range(24      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Optional[Any]          =       [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
								]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A				, even_batches=_A      )

								UpperCAmelCase_      :       Tuple          =       BatchSampler(range(24      )				, batch_size=4				, drop_last=_A      )
								# Expected shouldn't change
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A				, even_batches=_A      )

								# Check the shards when the dataset is not a round multiple of batch size.
								UpperCAmelCase_      :       Optional[Any]          =       BatchSampler(range(22      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Optional[int]          =       [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A				, even_batches=_A      )

								UpperCAmelCase_      :       List[str]          =       BatchSampler(range(22      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Any          =       [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A				, even_batches=_A      )

								# Check the shards when the dataset is not a round multiple of batch size or num_processes.
								UpperCAmelCase_      :       Union[str, Any]          =       BatchSampler(range(21      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Dict          =       [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A				, even_batches=_A      )

								UpperCAmelCase_      :       Tuple          =       BatchSampler(range(21      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       int          =       [
								    [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
								    [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
								]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A				, even_batches=_A      )

								# Check the shards when the dataset is very small.
								UpperCAmelCase_      :       Optional[int]          =       BatchSampler(range(2      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       Any          =       [[[0, 1]], []]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A				, even_batches=_A      )

								UpperCAmelCase_      :       Dict          =       BatchSampler(range(2      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       int          =       [[], []]
								self.check_batch_sampler_shards(_A				, _A				, split_batches=_A				, even_batches=_A      )




					def 		A      (  self  :		Dict      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
								UpperCAmelCase_      :       Optional[Any]          =       [BatchSamplerShard(_A				, 2				, _A				, even_batches=_A      ) for i in range(2      )]

								self.assertEqual(len(batch_sampler_shards[0]      )				, 3      )
								self.assertEqual(len(batch_sampler_shards[1]      )				, 2      )

								self.assertListEqual(list(batch_sampler_shards[0]      )				, [[0, 1, 2], [5, 6, 7, 8], [12, 13]]      )
								self.assertListEqual(list(batch_sampler_shards[1]      )				, [[3, 4], [9, 10, 11]]      )




					def 		A      (  self  :		Tuple				, _A  :		Tuple				, _A  :		Dict				, _A  :		List[str]				, _A  :		Optional[int]=False				, _A  :		List[Any]=2				, _A  :		List[str]=False      )    ->							Optional[int]:
								random.seed(_A      )
								UpperCAmelCase_      :       Tuple          =       list(_A      )

								UpperCAmelCase_      :       str          =       [
								    IterableDatasetShard(
								        _A				, batch_size=_A				, drop_last=_A				, num_processes=_A				, process_index=_A				, split_batches=_A				, )
								    for i in range(_A      )
								]
								UpperCAmelCase_      :       List[str]          =       []
								for iterable_dataset_shard in iterable_dataset_shards:
											# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
											random.seed(_A      )
											iterable_dataset_lists.append(list(_A      )      )

								UpperCAmelCase_      :       Tuple          =       batch_size // num_processes if split_batches else batch_size
								# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
								UpperCAmelCase_      :       Dict          =       iterable_dataset_lists[0]
								for l in iterable_dataset_lists[1:]:
											self.assertEqual(len(_A      )				, len(_A      )      )
											self.assertTrue(len(_A      ) % shard_batch_size == 0      )

								UpperCAmelCase_      :       Any          =       []
								for idx in range(0				, len(_A      )				, _A      ):
											for l in iterable_dataset_lists:
														observed += l[idx : idx + shard_batch_size]

								if not drop_last:
											while len(_A      ) < len(_A      ):
														reference += reference
								self.assertListEqual(_A				, reference[: len(_A      )]      )




					def 		A      (  self  :		Optional[int]      )    ->							str:
								UpperCAmelCase_      :       Union[str, Any]          =       42
								UpperCAmelCase_      :       str          =       RandomIterableDataset()

								self.check_iterable_dataset_shards(_A				, _A				, batch_size=4				, drop_last=_A				, split_batches=_A      )
								self.check_iterable_dataset_shards(_A				, _A				, batch_size=4				, drop_last=_A				, split_batches=_A      )
								self.check_iterable_dataset_shards(_A				, _A				, batch_size=4				, drop_last=_A				, split_batches=_A      )
								self.check_iterable_dataset_shards(_A				, _A				, batch_size=4				, drop_last=_A				, split_batches=_A      )

								# Edge case with a very small dataset
								UpperCAmelCase_      :       List[Any]          =       RandomIterableDataset(max_length=2      )

								self.check_iterable_dataset_shards(_A				, _A				, batch_size=4				, drop_last=_A				, split_batches=_A      )
								self.check_iterable_dataset_shards(_A				, _A				, batch_size=4				, drop_last=_A				, split_batches=_A      )
								self.check_iterable_dataset_shards(_A				, _A				, batch_size=4				, drop_last=_A				, split_batches=_A      )
								self.check_iterable_dataset_shards(_A				, _A				, batch_size=4				, drop_last=_A				, split_batches=_A      )




					def 		A      (  self  :		Any      )    ->							List[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       BatchSampler(range(16      )				, batch_size=4				, drop_last=_A      )
								UpperCAmelCase_      :       int          =       SkipBatchSampler(_A				, 2      )
								self.assertListEqual(list(_A      )				, [[8, 9, 10, 11], [12, 13, 14, 15]]      )




					def 		A      (  self  :		Optional[int]      )    ->							List[str]:
								UpperCAmelCase_      :       str          =       SkipDataLoader(list(range(16      )      )				, batch_size=4				, skip_batches=2      )
								self.assertListEqual([t.tolist() for t in dataloader]				, [[8, 9, 10, 11], [12, 13, 14, 15]]      )




					def 		A      (  self  :		Dict      )    ->							List[str]:
								UpperCAmelCase_      :       Any          =       DataLoader(list(range(16      )      )				, batch_size=4      )
								UpperCAmelCase_      :       List[str]          =       skip_first_batches(_A				, num_batches=2      )
								self.assertListEqual([t.tolist() for t in new_dataloader]				, [[8, 9, 10, 11], [12, 13, 14, 15]]      )




					def 		A      (  self  :		str      )    ->							Optional[Any]:
								UpperCAmelCase_      :       int          =       DataLoaderShard(list(range(16      )      )				, batch_size=4      )
								for idx, _ in enumerate(_A      ):
											self.assertEqual(dataloader.end_of_dataloader				, idx == 3      )

								# Test it also works on the second iteration
								for idx, _ in enumerate(_A      ):
											self.assertEqual(dataloader.end_of_dataloader				, idx == 3      )





					def 		A      (  self  :		List[Any]      )    ->							Dict:
								Accelerator()
								UpperCAmelCase_      :       Tuple          =       DataLoaderDispatcher(range(16      )				, batch_size=4      )
								for idx, _ in enumerate(_A      ):
											self.assertEqual(dataloader.end_of_dataloader				, idx == 3      )

								# Test it also works on the second iteration
								for idx, _ in enumerate(_A      ):
											self.assertEqual(dataloader.end_of_dataloader				, idx == 3      )








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase_					=   {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SwinForImageClassification",
      "SwinForMaskedImageModeling",
      "SwinModel",
      "SwinPreTrainedModel",
      "SwinBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSwinForImageClassification",
      "TFSwinForMaskedImageModeling",
      "TFSwinModel",
      "TFSwinPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swin import (
        SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinBackbone,
        SwinForImageClassification,
        SwinForMaskedImageModeling,
        SwinModel,
        SwinPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_swin import (
        TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSwinForImageClassification,
        TFSwinForMaskedImageModeling,
        TFSwinModel,
        TFSwinPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



